DoRA improves LoRA by decomposing weights into magnitude and direction and updating only direction with low-rank matrices, closing much of the gap to full fine-tuning.
Thirty-seventh Conference on Neural Information Processing Systems , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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Systematic evaluation finds cross-modal skill injection via model merging succeeds in instruction-following and cross-lingual scenarios but fails in mathematical reasoning, with TA and DARE methods outperforming others after hyperparameter analysis.
Introduces self-captioning and a Multimodal Interaction Gate to amplify redundant multimodal interactions, reporting 38.3% reduction in visual-induced errors and 16.8% consistency improvement.
citing papers explorer
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Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters
Systematic evaluation finds cross-modal skill injection via model merging succeeds in instruction-following and cross-lingual scenarios but fails in mathematical reasoning, with TA and DARE methods outperforming others after hyperparameter analysis.
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Self-Captioning Multimodal Interaction Tuning: Amplifying Exploitable Redundancies for Robust Vision Language Models
Introduces self-captioning and a Multimodal Interaction Gate to amplify redundant multimodal interactions, reporting 38.3% reduction in visual-induced errors and 16.8% consistency improvement.